Using word-embeddings for machine translation evaluation is appealing since they can be used to compute similarity between words or phrases in the same language (monolingual embeddings capture intrinsically synonymy or morphological closeness) or in two different languages (bilingual embeddings allow to directly compute a distance between two sentences in different languages).
We use the MultiVec [1]} toolkit for computing and managing the continuous representations of texts.
It includes word2vec [2]}, paragraph vector [3]} and bilingual distributed representations [4]} features.
